sas jmp customer churn excercise answers

With this the business analyst can solve standard statistical use cases on his own, thus freeing the data scientist from routine tasks and provide him more time for doing the heavy lifting. Does it really need a data scientist – an expensive and scarce resource not available to all companies – for a standard analysis like this, who in turn will not be overly excited by the simplicity of the problem? This is why SAP Analytics Cloud with Smart Predict offers a toolset to the business user that addresses common predictive use cases. Now that we have seen the importance of customer churn analysis to the business and have identified the data we could use we should try to identify the people who could do such a type of analysis. Although these are still important customer attributes in times of online shopping we can supplement this with data taken from our online shop like the number of visited pages, the average time spent on our homepage or abandoned shopping cards. Traditionally this analysis is based on customer data like age, gender and historic sales behavior. Therefore, customer churn analysis is one of the most popular use cases of predictive analytics. So why not try to prevent customers from seeking their luck elsewhere while they are still customers instead of trying to get them back after they have had (good) experiences with our competition? Wouldn’t it be great if we could identify customers who are at risk of leaving beforehand? Taking actions to prevent them from leaving instead of winning them back with a much bigger effort? Winning back customers who have lost faith in our services or products is a time consuming and costly task.

Sas jmp customer churn excercise answers